Decision trees are widely used in many fields of computation. In particular, they are useful for classifying data into various predefined types or classes, according to different characteristics of the data. For example and without limitation, in the field of advanced driving assistance and autonomous driving, it may be necessary or advantageous to classify different objects identified from a radar or video dataset, in order to determine the level of risk associated with each object. For instance it may be helpful to distinguish between a tree, a post-box, and a pedestrian: a tree may be assumed not to move once its location has been calculated; conversely, a pedestrian will require more detailed tracking as it is quite likely to move; moreover, a post-box may attract pedestrians towards it or may have pedestrians hidden from view behind it.
Another, non-limiting, example of the use of a decision tree is in the field of in-line inspection in manufacture processes. It may be required to check part-manufactured or completed articles, for quality control. This may be done using sensors—typically including cameras for visual inspection but potentially including other sensor types—by sensing properties of the article as it moves along a conveyor belt. Various characteristics (colour, size, shape, weight, or the like) of the article are sensed, and a processing engine such as a microprocessor is used to assess the characteristics to confirm whether or not the article meets specified quality requirements. The assessment may be carried out for each article, from the various input data, using a decision tree.
Conventional decision trees are processed serially, that is to say, one node of the tree is evaluated, and depending on the outcome of that evaluation, processing moves on to a subsequent node. As computational requirements become higher with increased volumes of input data or increased speed requirements, there has been a tendency to move towards parallel processing in many fields. However, conventionally decisions trees are not well-suited to parallel—and in particular to massively parallel—processing. Architectures and methods that allow or facilitate parallel processing in evaluating or classifying data according to decision trees are therefore desirable.